Data science has evolved from arbitrary numbers to an efficient way of managing data to derive significance.

The technology is moving towards complete digital transformation and massive quantities of data are expected to be produced in the future. That being said, we need experts who can understand, design and process the data in structured manner to make use of data. Data science has evolved from arbitrary numbers to an efficient way of managing data to derive significance.

Even so, with a multitude of choices out there, choosing the right curriculum and enrolling in the appropriate institute, suitable for your needs, is very daunting. While online courses are a great way for many to upskill, long-term opportunities from top universities and institutes continue to be the criterion for data scientists to test the scope and depth of their field knowledge.

Here are the five basic steps in today's world to become a master of data science:

1. To obtain a data science undergraduate degree or a similar field

In order to get a chance as an entry-level data scientist, one would need a bachelor's degree in data science, math, statistics and computer science. Degrees can also give internship opportunities. Even so, if you have already earned a bachelor's degree in a different area, through online short courses or basic training camps, you will need to concentrate on learning the skills required for the job.

The skill set is what employers are concerned about most. With a broad project portfolio that displays their technical qualifications and experience doing similar data science work, candidates with less technical or practically less appropriate degrees may mitigate this disadvantage.

2. Take courses and do them one at a time

A full course can make you a top-class data scientist. It must include all the expertise and tools required to become a comprehensive data scientist, hundreds of big projects and mentoring assistance. Jigsaw Academy's Postgraduate Diploma in Data Science (Full-time) with MAHE, is an example. This course deals with a deep foundation of subjects such as data science programming in Python, statistics, database management, data processing, data visualization, etc. It offers in-depth analysis of subjects such as data scraping, machine learning, analytics of big data, and more.

In the initial step of your journey, free courses are a good way to build on your experience. These courses give a great exposure to principles in data science. For instance, Python for Data Science. To construct machine learning models, Python is one of the most powerful and most commonly used languages. For Python beginners, this course is perfect and offers free certification as well.

3. Look for additional certifications in data science and post-graduate learning

Many universities have a lot of courses and training in data science knowledge and capabilities. This is to get an experience of the real world before you actually set forward. Here, you will get a lot of access to real-world data collection and cleaning tasks. Think about having a Ph.D. or a Masters. In contrast to completing a successful program from a good university, nothing beats improving the chances of having the right job.

4. Get an entry-level job

Companies also want to fill vacancies in data science at the entry – level positions. Check for roles like Junior Data Scientist or Junior Data Analyst. When searching for entry-level data science positions, system-specific training or certification in data-related fields (e.g. business intelligence applications, relational database management systems, data visualization tools, etc.) could help.

5. Know the advancements

Keep yourself updated with the latest changes in the data science industry. Every day, the amount of data produced by the world is growing and data science is also developing in keeping up with this exponential development. To maintain resource optimization, data scientists must learn ways to develop data monitoring and analyze applications. For data scientists to remain on top of the industry, continuous learning is essential. Look for possibilities for educational and professional advancement that will boost your data science profession.